Single-Cell RNA-Seq Data Analysis and Mining Platform

scRNA-Seq View can be used for both private single-cell data and public datasets. Private data are analyzed by our best-practice pipeline. Public data are either provided by the authors or have been analyzed and curated using our pipeline.

The single-cell RNA-Seq analysis pipeline integrates several popular tools (Seurat, Scanpy, Harmony, LIGER) and provides a semi-automated workflow for 1) processing of raw data, 2) batch-correction with several harmonization methods, 3) reference-based cell type label transfer, 4) multi-sample multi-condition single-cell level differential gene expression analysis. The results can be directly loaded into the scRNA web database.

We developed an integrated web database including single-cell RNA-Seq data from over 400 published datasets from multiple species and many tissues. It provides an efficient data management system for single cell datasets. Users can search the database by multiple attributes such as publication, key words, species and cell types. Advanced filter and across-dataset gene expression tools enable users to quickly find the datasets of interest and compare the expression of gene(s) across these.

Importantly, each scRNA dataset can be loaded into the cellxgene VIP tool, which enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types by leveraging more than 20 frequently applied plotting functions and high-level analysis methods in single cell research.

Figure legend: Cellxgene VIP includes analytical modules that provide essential functions for interactive visualization and generation of publication-ready plots. (a) Multi-tSNE/UMAP plot visually highlights which cells expressing cell markers on selected embedding (UMAP based on harmony batch correction in this example). (b) Dual-gene plot highlights cells express SYT1 and GAD1 (green SYT1 only, red GAD1 only, yellow co-expression of STY1 and GAD1), expression cutoff 2.2. (c) Stacked barplot demonstrates the fraction of each major cell type across each sample (C are Control and MS are MS patients). (d) Trackplot shows expression of lineage marker genes across individual cells in annotated clusters. (e) Violin plot shows the AQP4 gene expression across cell types. (f) Sankey diagram (a.k.a. Riverplot) provides quick and easy way to explore the inter-dependent relationship of variables in the MS snRNAseq dataset8. (g) Density plots shows expression of marker genes across annotated clusters and split across cell types. (h) Stacked violin and Dot plot are the key visualizations of selected cell markers across cell types. They highlight their selective expression and validates the scRNAseq approach and visualization method. (i) Command Line Interface (CLI) exposed by mini Jupyter Notebook to provide maximal flexibility of doing various analytics on the whole or sliced single cell dataset.